Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization

Xiangyang Kong; Yongqiang Zhao; Jonathan Cheung Wai Chan

doi:10.1109/IGARSS.2018.8518801

Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization

Xiangyang Kong, Yongqiang Zhao, Jonathan Cheung Wai Chan

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Classical matrix-based denoising methods for hyperspectral imagery (HSI) may cause spatial and spectral distortion. To improve denoising performance, a multi-linear weighted nuclear norm minimization was proposed for HSI denoising. By considering spectral continuity and inter-dependency of three unfolding modes, a multi-linear rank was proposed to model the spatial and spectral nonlocal similarity. To make the proposed method more tractable, a variable splitting based technique was used to solve the optimization problem. Experiment results reveal that the proposed method outperforms state-of-the-art methods both visually and quantitatively.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2717-2720
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8518801
State	Published - 31 Oct 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

Hyperspectral imagery denoising
Multi-linear rank
Multi-linear weighted nuclear norm
Nonlocal self-similarity

Access to Document

10.1109/IGARSS.2018.8518801

Cite this

Kong, X., Zhao, Y., & Chan, J. C. W. (2018). Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 2717-2720). Article 8518801 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8518801

Kong, Xiangyang ; Zhao, Yongqiang ; Chan, Jonathan Cheung Wai. / Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2717-2720 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization",

abstract = "Classical matrix-based denoising methods for hyperspectral imagery (HSI) may cause spatial and spectral distortion. To improve denoising performance, a multi-linear weighted nuclear norm minimization was proposed for HSI denoising. By considering spectral continuity and inter-dependency of three unfolding modes, a multi-linear rank was proposed to model the spatial and spectral nonlocal similarity. To make the proposed method more tractable, a variable splitting based technique was used to solve the optimization problem. Experiment results reveal that the proposed method outperforms state-of-the-art methods both visually and quantitatively.",

keywords = "Hyperspectral imagery denoising, Multi-linear rank, Multi-linear weighted nuclear norm, Nonlocal self-similarity",

author = "Xiangyang Kong and Yongqiang Zhao and Chan, {Jonathan Cheung Wai}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

year = "2018",

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series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Kong, X, Zhao, Y & Chan, JCW 2018, Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518801, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 2717-2720, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518801

Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization. / Kong, Xiangyang; Zhao, Yongqiang; Chan, Jonathan Cheung Wai.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2717-2720 8518801 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Classical matrix-based denoising methods for hyperspectral imagery (HSI) may cause spatial and spectral distortion. To improve denoising performance, a multi-linear weighted nuclear norm minimization was proposed for HSI denoising. By considering spectral continuity and inter-dependency of three unfolding modes, a multi-linear rank was proposed to model the spatial and spectral nonlocal similarity. To make the proposed method more tractable, a variable splitting based technique was used to solve the optimization problem. Experiment results reveal that the proposed method outperforms state-of-the-art methods both visually and quantitatively.

AB - Classical matrix-based denoising methods for hyperspectral imagery (HSI) may cause spatial and spectral distortion. To improve denoising performance, a multi-linear weighted nuclear norm minimization was proposed for HSI denoising. By considering spectral continuity and inter-dependency of three unfolding modes, a multi-linear rank was proposed to model the spatial and spectral nonlocal similarity. To make the proposed method more tractable, a variable splitting based technique was used to solve the optimization problem. Experiment results reveal that the proposed method outperforms state-of-the-art methods both visually and quantitatively.

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KW - Nonlocal self-similarity

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Kong X, Zhao Y, Chan JCW. Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2717-2720. 8518801. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8518801

Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization

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